LLaMA 3.3vsQwen3.8-Max-0902
LLaMA 3.3 | Qwen3.8-Max-0902 | |
|---|---|---|
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | 70B | 2.4T |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | — | 1M |
| API pricingUSD per 1M tokens · lower wins | ||
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.10DeepInfra | — |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.32DeepInfra | — |
| BenchmarksPublished by one model only | ||
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | — | 69.3% |
NL2Repo-BenchRepo-level code generation — Tests whether the AI can turn a natural-language requirement into working code across an entire repository, not just produce a single function or patch. Higher is better. | — | 64.9% |
QwenSWEBench V2Software engineering — Qwen's in-house coding benchmark, second version, built around complex real-world software-engineering tasks. Scores are not comparable with the first version. Higher is better. | — | 70% |
Terminal-Bench 3.0Agentic terminal coding — command-line task completion (v3.0, much harder task set) | — | 29% |
JobBenchProfessional tool use — Tests the AI on professional workplace tasks that require using real work tools — the kind of multi-step jobs an office worker handles. Higher is better. | — | 64% |
CoWorkBenchLong-horizon office work — Tests long-running office tasks across fields including computer science, finance, law, medicine, and other productivity work. Higher is better. | — | 76.1% |
Toolathlon-VerifiedPersonal tool use — Tests how well the AI uses everyday personal tools and apps to get things done — a human-checked version of Toolathlon. Higher is better. | — | 73.3% |
AutomationBenchBusiness workflows — Tests whether the AI can run real multi-step business workflows — the kind of end-to-end office processes companies want to automate — from start to finish. Higher is better. | — | 50.8% |
MMMU-ProMultimodal reasoning — A tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | — | 82.7% |
| Overview | ||
| Company | Meta | Qwen |
| Release date | Dec 6 2024 | Sep 2 2026 |
| Access | Open Weight | Proprietary |
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Frequently asked questions
LLaMA 3.3 and Qwen3.8-Max-0902 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 3.3 shipped 635 days before Qwen3.8-Max-0902, so benchmark comparisons should account for the intervening progress.
LLaMA 3.3 has 70B parameters, while Qwen3.8-Max-0902 has 2.4T. LLaMA 3.3 is open weight, while Qwen3.8-Max-0902 is proprietary.
Direct benchmark comparisons are unavailable — LLaMA 3.3 and Qwen3.8-Max-0902 don't publish scores on any of the same benchmarks.
LLaMA 3.3 was released by Meta on Dec 6 2024.
Qwen3.8-Max-0902 was released by Qwen on Sep 2 2026.
LLaMA 3.3 is an open weight model released by Meta. Qwen3.8-Max-0902 is a proprietary model released by Qwen.